Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics
Abstract Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology....
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202408353 |
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| author | Zongliang Guo Fenggang Li Hang Li Menglei Zhao Haobing Liu Haopu Wang Hanqi Hu Rongxin Fu Yao Lu Siyi Hu Huikai Xie Hanbin Ma Shuailong Zhang |
| author_facet | Zongliang Guo Fenggang Li Hang Li Menglei Zhao Haobing Liu Haopu Wang Hanqi Hu Rongxin Fu Yao Lu Siyi Hu Huikai Xie Hanbin Ma Shuailong Zhang |
| author_sort | Zongliang Guo |
| collection | DOAJ |
| description | Abstract Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active‐Matrix Digital Microfluidics (AM‐DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi‐target, collision‐free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL‐60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM‐DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology. |
| format | Article |
| id | doaj-art-a6818cee027f40d4966a3d5b5f84e9d7 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-a6818cee027f40d4966a3d5b5f84e9d72025-01-09T11:44:45ZengWileyAdvanced Science2198-38442025-01-01121n/an/a10.1002/advs.202408353Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital MicrofluidicsZongliang Guo0Fenggang Li1Hang Li2Menglei Zhao3Haobing Liu4Haopu Wang5Hanqi Hu6Rongxin Fu7Yao Lu8Siyi Hu9Huikai Xie10Hanbin Ma11Shuailong Zhang12Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 ChinaSchool of Medical Technology Beijing Institute of Technology Beijing 100081 ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 ChinaSchool of Integrated Circuits and Electronics Engineering Research Center of Integrated Acousto‐Opto‐Electronic Microsystems (Ministry of Education of China) Beijing Institute of Technology Beijing 100081 ChinaSchool of Medical Technology Beijing Institute of Technology Beijing 100081 ChinaSchool of Medical Technology Beijing Institute of Technology Beijing 100081 ChinaSchool of Integrated Circuits and Electronics Engineering Research Center of Integrated Acousto‐Opto‐Electronic Microsystems (Ministry of Education of China) Beijing Institute of Technology Beijing 100081 ChinaCAS Key Laboratory of Bio‐Medical Diagnostics Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou 215163 ChinaSchool of Integrated Circuits and Electronics Engineering Research Center of Integrated Acousto‐Opto‐Electronic Microsystems (Ministry of Education of China) Beijing Institute of Technology Beijing 100081 ChinaCAS Key Laboratory of Bio‐Medical Diagnostics Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou 215163 ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 ChinaAbstract Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label‐free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active‐Matrix Digital Microfluidics (AM‐DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi‐target, collision‐free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL‐60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM‐DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology.https://doi.org/10.1002/advs.202408353artificial intelligencecell sortingdigital microfluidicsdropletsingle cell researchlabel‐free |
| spellingShingle | Zongliang Guo Fenggang Li Hang Li Menglei Zhao Haobing Liu Haopu Wang Hanqi Hu Rongxin Fu Yao Lu Siyi Hu Huikai Xie Hanbin Ma Shuailong Zhang Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics Advanced Science artificial intelligence cell sorting digital microfluidics droplet single cell research label‐free |
| title | Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics |
| title_full | Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics |
| title_fullStr | Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics |
| title_full_unstemmed | Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics |
| title_short | Deep Learning‐Assisted Label‐Free Parallel Cell Sorting with Digital Microfluidics |
| title_sort | deep learning assisted label free parallel cell sorting with digital microfluidics |
| topic | artificial intelligence cell sorting digital microfluidics droplet single cell research label‐free |
| url | https://doi.org/10.1002/advs.202408353 |
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